{
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  {
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   "source": [
    "# Introduction to Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Introduction\n",
    "* Pandas is a newer package built on top of NumPy\n",
    "* Pandas provides an efficient implementation of a DataFrame\n",
    "* DataFrames are essentially multidimensional arrays with attached row and column labels, and often with heterogeneous types and/or missing data. \n",
    "* Pandas implements a number of powerful data operations familiar to users of both database frameworks and spreadsheet programs\n",
    "* documentation: http://pandas.pydata.org/pandas-docs/stable/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
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   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use(\"default\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "data = pd.DataFrame(np.random.randn(50, 5))\n",
    "# Make sure the directory exists\n",
    "os.makedirs('data', exist_ok=True)\n",
    "data.to_csv('data/test_csv1.csv', sep=';', index_label=['a', 'b', 'c', 'd', 'e', 'f'], decimal=',')\n",
    "data.to_excel('data/test_excel1.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Read csv-data\n",
    "\n",
    "http://pandas.pydata.org/pandas-docs/stable/io.html#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
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       "                                                                                        a;b;c;d;e;f;0;1;2;3;4\n",
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     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "data_csv = pd.read_csv('data/test_csv.csv')\n",
    "data_csv.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
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       "   a         b         c         d         e\n",
       "0  0 -0.424687  0.552284 -2.065893 -0.788852\n",
       "1  1  0.042881 -0.774575  0.127811 -0.366670\n",
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       "3  3 -2.849678 -0.141530 -1.252892 -0.211926\n",
       "4  4  1.237444  0.393888 -0.207607 -0.742593"
      ]
     },
     "execution_count": 22,
     "metadata": {},
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    }
   ],
   "source": [
    "# sometimes you have to give some more details to read the csv correctly\n",
    "\n",
    "data_csv = pd.read_csv('data/test_csv.csv', sep=';', decimal=',', usecols=(0, 1, 2, 3, 4))\n",
    "data_csv.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Basic Operations on Data Frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50, 5)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# shape of the data frame\n",
    "\n",
    "data_csv.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "slideshow": {
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       "     a         b         c         d         e\n",
       "0    0 -0.424687  0.552284 -2.065893 -0.788852\n",
       "2    2  0.414652  0.036938  3.063914 -1.147287\n",
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       "6    6  1.493295  0.319811  1.947756 -1.795701\n",
       "13  13  1.683426  0.512369 -0.067183  0.615619"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# filter rows of the data frame\n",
    "\n",
    "data_csv = data_csv.query('a < 0.8 | c > 0')\n",
    "data_csv.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(18, 3)\n"
     ]
    },
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       "     a         b         c\n",
       "0    0 -0.424687  0.552284\n",
       "2    2  0.414652  0.036938\n",
       "4    4  1.237444  0.393888\n",
       "6    6  1.493295  0.319811\n",
       "13  13  1.683426  0.512369"
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     "execution_count": 25,
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   ],
   "source": [
    "# filter columns of the data frame\n",
    "\n",
    "data_csv = data_csv.filter(items=['a', 'b', 'c'])\n",
    "print(data_csv.shape)\n",
    "data_csv.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
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       "     a         b         c\n",
       "2    2  0.414652  0.036938\n",
       "39  39 -0.302028  0.091859\n",
       "27  27  0.942635  0.123114\n",
       "31  31  0.300276  0.128002\n",
       "21  21  0.371740  0.128198"
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     "metadata": {},
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   ],
   "source": [
    "# sort the data frame\n",
    "\n",
    "data_csv = data_csv.sort_values(by=['c'])\n",
    "data_csv.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
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       "      <th>13</th>\n",
       "      <td>1.683426</td>\n",
       "      <td>0.512369</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           b         c\n",
       "a                     \n",
       "0  -0.424687  0.552284\n",
       "2   0.414652  0.036938\n",
       "4   1.237444  0.393888\n",
       "6   1.493295  0.319811\n",
       "13  1.683426  0.512369"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set index\n",
    "\n",
    "data_csv = data_csv.set_index(keys=['a'])\n",
    "data_csv = data_csv.sort_values(by=['a'])\n",
    "data_csv.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# save a new csv\n",
    "\n",
    "data_csv.to_csv('data/test_neue_csv.csv', sep=';', decimal=',')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Statistical Operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b    -0.763346\n",
       "c    10.802686\n",
       "dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_csv.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b   -0.042408\n",
       "c    0.600149\n",
       "dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_csv.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b    0.234007\n",
       "c    0.444116\n",
       "dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_csv.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b    1.105430\n",
       "c    0.675003\n",
       "dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_csv.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    18.000000\n",
       "mean     -0.042408\n",
       "std       1.105430\n",
       "min      -2.240860\n",
       "25%      -0.995039\n",
       "50%       0.234007\n",
       "75%       0.745726\n",
       "max       1.683426\n",
       "Name: b, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_csv['b'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Plotting functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='a'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_csv.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "data_csv['b'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Read excel-data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-0.424687</td>\n",
       "      <td>0.552284</td>\n",
       "      <td>-2.065893</td>\n",
       "      <td>-0.788852</td>\n",
       "      <td>2.181749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.042881</td>\n",
       "      <td>-0.774575</td>\n",
       "      <td>0.127811</td>\n",
       "      <td>-0.366670</td>\n",
       "      <td>0.795794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.414652</td>\n",
       "      <td>0.036938</td>\n",
       "      <td>3.063914</td>\n",
       "      <td>-1.147287</td>\n",
       "      <td>-0.731876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-2.849678</td>\n",
       "      <td>-0.141530</td>\n",
       "      <td>-1.252892</td>\n",
       "      <td>-0.211926</td>\n",
       "      <td>-0.104684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1.237444</td>\n",
       "      <td>0.393888</td>\n",
       "      <td>-0.207607</td>\n",
       "      <td>-0.742593</td>\n",
       "      <td>0.166191</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0         0         1         2         3         4\n",
       "0           0 -0.424687  0.552284 -2.065893 -0.788852  2.181749\n",
       "1           1  0.042881 -0.774575  0.127811 -0.366670  0.795794\n",
       "2           2  0.414652  0.036938  3.063914 -1.147287 -0.731876\n",
       "3           3 -2.849678 -0.141530 -1.252892 -0.211926 -0.104684\n",
       "4           4  1.237444  0.393888 -0.207607 -0.742593  0.166191"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_excel = pd.read_excel('data/test_excel.xlsx')\n",
    "data_excel.head()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "RL25",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
